What is cell painting?

Cell painting involves a set of fluorescent reagents that are used to visualize and analyze the spatial organization of cellular structures and components. The assay is often used during drug screening to profile small molecules or other compounds on stained proteins in living cells. The pattern of cellular staining may revel morphological differences in samples treated by small molecule compounds.

Cell painting evaluates several organelle-based and subcellular-based measurements, including:

  • Nucleus
  • Nucleolus
  • ER/Golgi
  • Mitochondria
  • Actin cytoskeleton
  • Plasma membrane

Cell painting allows researchers to study:

  • Dynamic organization of proteins
  • Cell viability
  • Cell proliferation and toxicity
  • DNA damage
App note cover

Get more morphological profiling parameters with cell painting

The cell painting high-content experiment provides approximately 1,500 measurements which can be extracted from each cell based on changes in size, shape, texture, and fluorescence intensity.

Download the cell painting application note

What is the cell painting workflow?

  1. Plate cells—cells are plated into 96- or 384-well plates at the desired confluency
  2. Treatment/perturbation—causes desired phenotype of interest, either by chemical or genetic means
  3. Fixation and staining—after treatment, cells are fixed, permeabilized and stained for the desired markers using individual reagents or a cell painting kit.
  4. Image acquisition—the plate is sealed and loaded into an HCS-imager and images are acquired from every well. Image acquisition time will vary based on the number of images per well sampled, sample brightness, and the extent of sampling in the z-dimension.
  5. Analysis—using automated software or previously designed scripts, features are extracted from the six-channel data to indicate a diverging phenotype, and these features are analyzed by cluster analysis (or a similar technique) to create several phenotypic profiles.

For an example of the cell painting workflow and protocol, see this cell painting application note.

Cell painting with an HCS system

Data for cell painting are typically acquired using a high-content screening (HCS) imaging system. HCS systems employ fluorescent imaging not unlike a traditional fluorescence microscope but are designed specifically to image multi-well (typically 96- or 384-well) plates at maximum speed for highest data throughput. A combination of traditional widefield and confocal fluorescence capabilities are often equipped, the latter being necessary for thick samples like organoids/spheroids or where maximum brightness and sensitivity is paramount.

Data acquisition is only the starting point. Processing those large data sets is often the rate limiting step during an experiment. Data processing can be performed using a variety of computing methods, but the goal is to elucidate significantly different phenotypic features between cellular populations to identify populations that are unique. This level of data processing has only recently become possible as data sets can now reach into the tens of terabytes.

Figure 1. Representative image and data readout from an HCS data set acquired on a Cellinsight CX7 LZR Pro system.

Sample cell painting data

3-panel fluorescence image showing diverging phenotypes between untreated, 10 µM staurosporine, and 1 µM Taxol cell populations

Figure 2. Phenotype comparisons of untreated vs. pharmacological control exposure in U2OS cells. Cells were treated with compounds of interest at 1–100 μM final concentrations for 48 hours in 96-well imaging plates. After treatment with the compounds, cells were immediately labeled using the Image-iT Cell Painting Kit and analyzed using the CellInsight CX7 LZR Pro instrument.

Figure 3. Example of a cell painting experiment with modified reagents. Reagents can be modified for specific experimental requirements, as is the case in the above showing the evaluation of cardiomyocyte mechanisms of action. H9c2 cells were seeded at 2,000 cells per well in 96-well plates. Two hours after seeding, the cells were treated with the listed compounds for 4, 24, or 96 hours before immunofluorescent labeling and analysis using the Cellomics Compartmental Analysis bioapplication.

Ordering information

Cell painting kit ordering information

The Image-iT Cell Painting Kit offers:

An optimized format for high-content screening—reagents available in two quantities, each pre-measured to provide precisely the amount of reagent needed for 2 or 10 full, multi-well plate experiments, simplifying sample preparation

Demonstrated performance—based on the same reagents used in the original cell painting Nature Protocols paper submitted by the Broad Institute [1] including established, bright Invitrogen Alexa Fluor dyes

Cell Painting Kit product photo

Individual reagents ordering information

Cell structureUVVioletGreenOrangeRedDeep redNIR
ActinAlexa Fluor 350 Phalloidin (A22281)

AlexaFluor Plus 405 Phalloidin (A30104)


Alexa Fluor 488 Phalloidin (A12379)

Alexa Fluor Plus 555 Phalloidin (A30106)Alexa Fluor 594 Phalloidin (A12381)Alexa Fluor Plus 647 Phalloidin (A30107)Alexa Fluor Plus 750 Phalloidin (A30105)
CellMask Green Actin Tracking Stain (A57243)CellMask Orange Actin Tracking Stain (A57244)CellMask Deep Red Actin Tracking Stain (A57245)
Plasma membraneWheat germ agglutinin, Alexa Fluor 350 conjugate (W11263)Wheat germ agglutinin, Alexa Fluor plus 405 Conjugate (W56132)

Wheat germ agglutinin, Alexa Fluor 488 conjugate (W11261)

Wheat germ agglutinin, Alexa Fluor 555 conjugate (W32464)Wheat germ agglutinin, Alexa Fluor 594 conjugate (W11262)Wheat germ agglutinin, Alexa Fluor 647 conjugate (W32466)Wheat germ agglutinin, Alexa Fluor plus 770 Conjugate (W56134)
CellMask Green Plasma Membrane Stain (C37608)Wheat germ agglutinin, Alexa Fluor Plus 568 Conjugate (W56133)CellMask Deep Red Plasma Membrane Stain (C10046)CellMask NIR Plasma Membrane Stain (C56129)
CellMask Orange Plasma Membrane Stain (C10045)
Mitochondria   MitoTracker Orange CMTMRos (M7510)MitoTracker Red CM-H2XRos (M7513)MitoTracker Deep Red FM (M22426) 
ERConcanavalin A, Alexa Fluor 350 (C11254)Concanavalin A, Alexa Fluor Plus 405 (C56126)Concanavalin A, Alexa Fluor 488 (C11252) Concanavalin A, Alexa Fluor 594 (C11253)Concanavalin A, Alexa Fluor 647 (C21421)Concanavalin A, Alexa Fluor Plus 750 (C56127)
NucleusHCS NuclearMask Blue (H10325)Hoechst 34580 (H21486)SYTO 9 Green Fluorescent Nucleic Acid Stain (S34854) SYTO 82 Orange Fluorescent Nucleic Acid Stain (S11363)HCS NuclearMask Red (H10326)HCS NuclearMask Deep Red (H10294) 
Hoechst 33342 (H3570)SYTO 59 Red Nucleic Acid Stain (S11341)SYTO Deep Red (S34901)
Nucleolus  SYTO 14 Green Fluorescent Nucleic Acid Stain (S7576)    
Fixable functional markers for modified cell painting experiments
Apoptosis/Caspase  CellEvent Caspase-3/7 Green Detection Reagent (lyophilized) (C10432) CellEvent Caspase-3/7 Red Detection Reagent (lyophilized) (C10430)  
Oxidative stress  CellROX Green Reagent, for oxidative stress detection (C10444)  CellROX Deep Red Reagent, for oxidative stress detection (C10422) 
Autophagy  Premo Autophagy Tandem Sensor RFP-GFP-LC3B Kit (P36239)Premo Autophagy Tandem Sensor RFP-GFP-LC3B Kit (P36239)Premo Autophagy Tandem Sensor RFP-GFP-LC3B Kit (P36239)  
Premo Autophagy Sensor GFP-p62 Kit (P36240)Premo Autophagy Sensor RFP-p62 Kit (P36241)
Premo Autophagy Sensor LC3B-GFP (BacMam 2.0) (P36235)Premo Autophagy Sensor LC3B-RFP (BacMam 2.0) (P36236)

Cell painting publications

Bray et al. (below) is the publication on which the cell painting technique is based. This where the term “cell painting” was first used in literature and is considered the standard protocol.

  1. Bray, MA., Singh, S., Han, H. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc 11, 1757–1774 (2016). PMID: 27560178 

Schiff et al. (below) describes how machine-learning using a neural network was employed to discover Parkinson’s Disease phenotypes in cultured patient fibroblasts compared against healthy controls. The authors report this this method is accurate enough to reliably differentiate between healthy, sporadic, and LRRK2/genetic–driven disease states.

  1. Schiff, L., Migliori, B., Chen, Y. et al. Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts. Nat Commun 13, 1590 (2022). PMID: 35338121
Stylesheet for Classic Wide Template adjustments
Stylesheet for Classic Wide Template adjustments

For Research Use Only. Not for use in diagnostic procedures.